These are the three types of bias that explain all the fake news, pseudoscience, and other junk in your News Feed

Indiana University researchers “have found that steep competition for users’ limited attention means that some ideas go viral despite their low quality — even when people prefer to share high-quality content.”

Our research has identified three types of bias that make the social media ecosystem vulnerable to both intentional and accidental misinformation. That is why our Observatory on Social Media at Indiana University is building tools to help people become aware of these biases and protect themselves from outside influences designed to exploit them.

Bias in the brain

Cognitive biases originate in the way the brain processes the information that every person encounters every day. The brain can deal with only a finite amount of information, and too many incoming stimuli can cause information overload. That in itself has serious implications for the quality of information on social media. We have found that steep competition for users’ limited attention means that some ideas go viral despite their low quality — even when people prefer to share high-quality content.

To avoid getting overwhelmed, the brain uses a number of tricks. These methods are usually effective, but may also become biases when applied in the wrong contexts.

To counter this bias, and help people pay more attention to the source of a claim before sharing it, we developed Fakey, a mobile news literacy game (free on Android and iOS) simulating a typical social media news feed, with a mix of news articles from mainstream and low-credibility sources. Players get more points for sharing news from reliable sources and flagging suspicious content for fact-checking. In the process, they learn to recognize signals of source credibility, such as hyperpartisan claims and emotionally charged headlines.

Screenshots of the Fakey game.

Bias in society

Another source of bias comes from society. When people connect directly with their peers, the social biases that guide their selection of friends come to influence the information they see.

The tendency to evaluate information more favorably if it comes from within their own social circles creates “echo chambers” that are ripe for manipulation, either consciously or unintentionally. This helps explain why so many online conversations devolve into “us versus them” confrontations.

To study how the structure of online social networks makes users vulnerable to disinformation, we built Hoaxy, a system that tracks and visualizes the spread of content from low-credibility sources, and how it competes with fact-checking content. Our analysis of the data collected by Hoaxy during the 2016 U.S. presidential elections shows that Twitter accounts that shared misinformation were almost completely cut off from the corrections made by the fact-checkers.

When we drilled down on the misinformation-spreading accounts, we found a very dense core group of accounts retweeting each other almost exclusively — including several bots. The only times that fact-checking organizations were ever quoted or mentioned by the users in the misinformed group were when questioning their legitimacy or claiming the opposite of what they wrote.

A screenshot of a Hoaxy search shows how common bots — in red and dark pink — are spreading a false story on Twitter.

Bias in the machine

The third group of biases arises directly from the algorithms used to determine what people see online. Both social media platforms and search engines employ them. These personalization technologies are designed to select only the most engaging and relevant content for each individual user. But in doing so, it may end up reinforcing the cognitive and social biases of users, thus making them even more vulnerable to manipulation.

Our own research shows that social media platforms expose users to a less diverse set of sources than do non-social media sites like Wikipedia. Because this is at the level of a whole platform, not of a single user, we call this the homogeneity bias.

Another important ingredient of social media is information that is trending on the platform, according to what is getting the most clicks. We call this popularity bias, because we have found that an algorithm designed to promote popular content may negatively affect the overall quality of information on the platform. This also feeds into existing cognitive bias, reinforcing what appears to be popular irrespective of its quality.

A screenshot of the Botometer website, showing one human and one bot account.

To study these manipulation strategies, we developed a tool to detect social bots called Botometer. Botometer uses machine learning to detect bot accounts, by inspecting thousands of different features of Twitter accounts, like the times of its posts, how often it tweets, and the accounts it follows and retweets. It is not perfect, but it has revealed that as many as 15 percent of Twitter accounts show signs of being bots.

Using Botometer in conjunction with Hoaxy, we analyzed the core of the misinformation network during the 2016 U.S. presidential campaign. We found many bots exploiting both the cognitive, confirmation and popularity biases of their victims and Twitter’s algorithmic biases.

These bots are able to construct filter bubbles around vulnerable users, feeding them false claims and misinformation. First, they can attract the attention of human users who support a particular candidate by tweeting that candidate’s hashtags or by mentioning and retweeting the person. Then the bots can amplify false claims smearing opponents by retweeting articles from low-credibility sources that match certain keywords. This activity also makes the algorithm highlight for other users false stories that are being shared widely.

Understanding complex vulnerabilities

Even as our research, and others’, shows how individuals, institutions, and even entire societies can be manipulated on social media, there are many questions left to answer. It’s especially important to discover how these different biases interact with each other, potentially creating more complex vulnerabilities.

Tools like ours offer internet users more information about disinformation, and therefore some degree of protection from its harms. The solutions will not likely be only technological, though there will probably be some technical aspects to them. But they must take into account the cognitive and social aspects of the problem.

Ciampaglia and Filippo Menczer, Giovanni Luca. "These are the three types of bias that explain all the fake news, pseudoscience, and other junk in your News Feed." Nieman Journalism Lab. Nieman Foundation for Journalism at Harvard, 20 Jun. 2018. Web. 21 Jan. 2019.

APA

Ciampaglia and Filippo Menczer, G. (2018, Jun. 20). These are the three types of bias that explain all the fake news, pseudoscience, and other junk in your News Feed. Nieman Journalism Lab. Retrieved January 21, 2019, from http://www.niemanlab.org/2018/06/these-are-the-three-types-of-bias-that-explain-all-the-fake-news-pseudoscience-and-other-junk-in-your-news-feed/

Chicago

Ciampaglia and Filippo Menczer, Giovanni Luca. "These are the three types of bias that explain all the fake news, pseudoscience, and other junk in your News Feed." Nieman Journalism Lab. Last modified June 20, 2018. Accessed January 21, 2019. http://www.niemanlab.org/2018/06/these-are-the-three-types-of-bias-that-explain-all-the-fake-news-pseudoscience-and-other-junk-in-your-news-feed/.